CLT_Six Sigma Glossario

8/3/2019 CLT_Six Sigma Glossario

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Benchmarking

An improvement process whereby a company measures its performance against that of

best-in-class companies, determines how those companies achieved their performancelevels, and uses the information to improve its own performance.

Black Belt

Full-time Six Sigma project leader who is certified following a four-month training and

application program and successful completion of two Six Sigma Projects, the first under

the guidance of a Master Black Belt, the second more autonomously.

Breakthrough Strategy

The data driven, Six Sigma process improvement strategy involving four phases:

Measure, Analyze, Improve and Control.

Cause

That which produces an effect or brings about change.

Cause-And-Effect Diagram

A schematic sketch, usually resembling a fishbone, which illustrates the main causes and

subcauses leading to an effect (symptom). Also known as Fishbone Diagram.

ChampionMember of the senior Aircraft Engines staff who has undergone extensive Six Sigma

training. Champions provide direction, resources and support to the Six Sigma effort and

approve and review projects.

Characteristic

A definable or measurable feature of a process, product or variable.

Control Chart

A graphical rendition of a characteristics performance across time in relation to itsnatural limits and central tendency.

Correlation

The determination of the effect of one variable upon another in a dependent situation.

Cp

A widely used capability index for process capability studies. It may range in value from

zero to infinity with a larger value indicating a more capable process. Six Sigma

represents Cp of 2.0.


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Effect

That which was produced by a cause.

Experiment

A test under defined conditions to determine an unknown effect; to illustrate or verify a

known law; to test or establish a hypothesis.

Experimental Error

A test under defined conditions to determine an unknown effect; to illustrate or verify a

known law; to test or establish a hypothesis.

Factory ProcessesFor Six Sigma purposes, defined as design, manufacturing, assembly or test processes

which directly impact hardware (see also transaction processes).

Fishbone Diagram

A schematic sketch, usually resembling a fishbone, which illustrates the main causes and

subcauses leading to an effect (symptom). Also known as Cause-And-Effect Diagram.

Failure Mode Effects Analysis (FMEA)

A process in which each potential failure mode in every sub-item of an item is analyzedto determine its effect on other sub-items and on the required function of the item.

Five Ms

Major sources of variation: manpower, machine, method, material and measurement.

Additionally, environment is considered to be a source of variation.

Frequency Distribution

The pattern or shape formed by the group of measurements in a distribution.

Gage Repeatability & Reproducibility (Gage R&R)

A measurement system evaluation to determine equipment variation and appraiser

variation. This study is critical to ensure that the collected data is accurate.

Histogram

Vertical display of a population distribution in terms of frequencies; a formal method of

plotting a frequency distribution.

Independent Variable

A controlled variable; a variable whose value is independent of the value of anothervariable.


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Interaction

When the effects of a factor A are not the same at all levels of another factor B.

Lower Control Limit

A horizontal dotted line plotted on a control chart which represents the lower process

limit capabilities of a process.

Master Black Belt

An expert in quality techniques specially trained to advise leaders, facilitate quality teams

and accelerate process improvement. Master Black Belts select, train and mentor Black

Belts; develop and implement the Six Sigma deployment plan; and select and ensure

completion of Six Sigma projects.

Nonconformity

A condition within a unit which does not conform to some specification, standard, and/or

requirement; often referred to as a defect; any given nonconforming unit can have the

potential for more than one nonconformity.

Normal Distribution

A continuous symmetrical density function characterized by a bell-shaped curve, e.g.,

distribution of sampling averages.

Pareto Diagram

A chart which ranks, or places in order, common occurrences.

Primary Control Variables

The major independent variables used in the experiment.

Probability

The chance of something happening; the percent or number of occurrences over a largenumber of trails.

Process

A particular method of doing something, generally involving a number of steps or

operations.

Process Capability

The relative ability of any process to produce consistent results centered on a desired

target value when measured over time.


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Process Control Chart

Any of a number of various types of graphs upon which data are plotted against specific

control limits.

Process Map

Flow chart to analyze a process by breaking it down into its component steps, and then

gaining a better understanding of the process, step-by-step.

Process Spread

The range of values which a given process characteristic displays; this particular term

most often applies to the range but may also encompass the variance. The spread may be

based on a set of data collected at a specific point in time or may reflect the variability

across a given amount of time.

Quality Functional Deployment (QFD)

Structured methodology to identify and translate customer needs and wants into technical

requirements and measurable features and characteristic. This tool is used to identify

Critical to Quality Characteristics (CTQCs).

Random

Selecting a sample so each item in the population has an equal chance of being selected;lack of predictability; without pattern.

Random Cause

A source of variation which is random; a change in the source (trivial many variables)

will not produce a highly predictable change in the response (dependent variable), e.g., a

correlation does not exist; any individual source of variation results in a small amount of

variation in the response; cannot be economically eliminated from a process; an inherent

natural source of variation.

Random Variation

Variations in data which result from causes which cannot be pinpointed or controlled.

Regression Analysis

A statistical technique for determining the relationship between one response and one or

more independent variables.

Robust

The condition or state in which a response parameter exhibits hermetically to externalcause of a nonrandom nature; e.g., impervious to perturbing influence.


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Rolled Yield

The combined resulting quality level, stated as a percent acceptable, that occurs when

several processes known to produce defects at some rate are combined to produce a

product. For example, a product that requires 100 steps, each of which produces a yield

of 98.78% will produce a rolled yield of 0%, that is, no acceptable products.

Scatter Diagram

A diagram that displays the relationships between two variables.

Sigma

Standard deviation; an empirical measure based on the analysis of random variation in a

standard distribution of values; a uniform distance from the mean or average value such

that 68.26% of all values are within 1 sigma on either side of the mean, 95.44% are

within 2 sigma, 99.73% are within 3 sigma, 99.9% are within 4 sigma and so forth.

Sigma Level

A statistical estimate of the number of defects that any process will produce equivalent to

defects per million opportunities for that process.

Six Sigma Quality

A combination of verified customer requirements reflected in robust designs and matchedto the capability of production processes that creates products with fewer then 3.4 defects

per million opportunities to make a defect. World-class quality. A collection of tools

and techniques for raising quality to worked-class levels.

Stable Process

A process which i free of assignable causes, e.g., in statistical control.

Standard Deviation

A statistical index of variability which describes the spread.

Statistical Control

A quantitative condition which describes a process that is free of assignable/special

causes of variation, e.g., variation in the central tendency and variance. Such a condition

is most often evidenced on a control chart.

Statistical Process Control

The application of statistical methods and procedures relative to a process and a given set

of standards.


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Transaction Processes

For Six Sigma purposes, defined as any business process that contributes to customer

satisfaction or impacts operating efficiency and which is designated by a vice president or

by GE Corporate as a focus for process improvement. Such efforts will be led by the

process owner, with teams being led by specially trained transaction project leaders

and/or by certified Black Belts.

Transaction Project Leader

An individual designated to lead a transaction process improvement project. Transaction

project leaders attend a four-day course in specific Six Sigma tools and tactics.

Upper Control Limit

A horizontal line on a control chart (usually dotted) which represents the upper limits of

process capability.

Variable

A characteristic that may take on different values.

Variables Data

A numerical measurement made at the interval or ratio level; quantitative data, e.g..,

ohms, voltage, diameter; subdivisions, of the measurement scale are conceptuallymeaningful, e.g.., 1.6478 volts.

Variation

Any quantifiable difference between individual measurements; such differences can be

classified as being due to common causes (random) or special causes (assignable).

Xs

Designation in Six Sigma terminology for those variables which are independent, root

causes; as opposed to Ys which are dependent outputs of a process. Six Sigma focuseson measuring and improving Xs, to see subsequent improvement in Ys.

X & R Charts

A control chart which is a representation of process capability over time; displays the

variability in the process average and range across time.

Ys

Designation in Six Sigma terminology for those variables which are dependent outputs of

a process, as opposed to Xs which are independent root causes.


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6Ms - Man, Machines, Materials, Methods, Measurement, Mother Nature

ANOVA - Analysis of Variance

BB - Black Belts

C&E Matrix - Cause & Effect Matrix

CAP - Change Acceleration Process

C&E - Cause & Effect

COPQ - Cost of Poor Quality

COQ - Cost of Quality

Cp - Capability Process Index (Ideal) - Pooled

Cpk - Capability Process Index (Real) - Pooled

CTQ - Critical to Quality

CUSUM - Cumulative Sum

DF - Degrees of Freedom

DFM - Design for Manufacturing

DFSS - Design for Six Sigma

DOE - Design of Experiments

DPM - Defects per Million

DPMO - Defects per Million Opportunities

DPO - Defects per OpportunitiesDPU - Defects per Unit

EVOP - Evolutionary Operation

EWMA - Exponential Weight Moving Average

FMEA - Failure Mode & Effect Analysis

GAGEAOV - Gage Analysis of Variance

GRR - Gage Repeatability & Reproducibility

IDOV - Identify, Design, Optimize, Validate

IQR - Inter Quartile Range

ISO - International Organization for Standardization

KNP - Key Noise Parameters

KPI (Factors) - Key Process Inputs

KPIV (KCP) - Key Process Input Variable (Key Control Parameter)

KPOV or - Key Process Output Variable(Response)

LCL - Lower Controls Limits

LSL - Lower Specification Limits

MAIC - Measurement, Analysis, Improvement, Control

MBB - Master Black Belt


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MBNQA - Malcolm Baldrich National Quality Award

MGF - Minitab Graph File

MSA - Measurement System Analysis

MTB - Minitab

MTW - Minitab Worksheet

NPI - New Product Introduction

OJT - On the Job Training

P(ND) - Probability (Not Defective)

PEAR - Process, Engineering, Application, Regulatory CTQs

Pp - Capability Process Index (Ideal) - OverallPpk - Capability Process Index (Real) - Overall

PPM - Parts per Million

QA - Quality Assurance

QFD - Quality Functional Deployment

P/T Ratio - Precision / Tolerance Ratio

ROI - Return of Investment

RPN - Risk Priority Number

RSM - Response Surface Methodology

RTY - Rolled Throughput Yield

SOP - Standard Operating Procedure

SOV - Source of Variation

SPC - Statistical Process Control

SQC - Statistical Quality Control

T - Target

TCS - Total Customer Satisfaction

TOP - Total Opportunities

TQL - Total Quality Leadership

TQM - Total Quality Management

UCL - Upper Control Limits

USL - Upper Specification Limits

WIP - Work in Process

XLS - Excel Spreadsheet

Zlt - Z-long term

ZST - Z-short term


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7 = Summation; i.e., 1 + 2 + 3 + 4 + 5 = 15

! = Factorial; i.e. 5! = 5 x 4 x 3 x 2 x 1 = 120

e = Natural constant = 2.7183

g = Total number of subgroups.

i = The ith element in a string of 1, 2, 3, 4, -- i

j = The jth element in a string of 1, 2, 3, 4, -- j

n = Subgroup size (for high volume production, the range for n would

normally be between 3 and 10.

R = Range = difference (subtraction) between the maximum and minimum

measurements observed/recorded for a subgroup

R = Average of subgroup ranges = R; g

S = Standard deviation =W

X = A variable measurement made on an individual characteristic and on an

individual unit (often a process output variable) recorded onto a data log

or control chart.

Note: X is also used in another sense to denote the variables that

cause process variation.

X = Average of the X observations associated with a subgroup of size n

X = Average of observations over all subgroups = X ng

WLT = Standard deviation of the total population over a long period of time.

/j = 1

g

X /ni = 1

i = 1j = 1i i /

n X i


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WLT = Estimate of long-term standard deviation =

W = Standard deviation of an individual subgroup =

WST = Standard deviation of a population over a short period of time

WST=

Estimate for short-term standard deviation WST ; WST=

WW = Pooled standard deviation =

W2 = Variance

u = Process average or mean =X

u = Subgroup average or mean =X

Y = A process output variable - may likely be a CTQ

YRT = Rolled thruput yield

Cp = Short term process capability assuming no shift. Cp = 3 X ZST

Cpk = Short term process capability including mean shift occurring in the

process.

Z ST = Number of short-term standard deviations (WST) that fit between thespecification center and the specification limit (in either direction)

j


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Z LT = Number of long-term deviations (WLT) that fit between the observedprocess average (X) and the closest specification limit.

ZLT=

G2 = CHI square distribution - Used for hypothesis testing as follows:

Test for independence (used to test for independent relationship

between two discrete variables)

Goodness of fit (used to determine if the data fits an assured

distribution)

Establishing the confidence interval for standard deviation

F = F distribution - associated with hypothesis testing of standard

deviation between two or more process distributions.

T = T distribution - associated with hypothesis testing of the means

(averages) between two distributions (when sample sizes are less than

100).

X

(1SL -X)

W LT


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ABSCISSA The horizontal axis of a graph.

ACCEPTANCE REGION The region of values for which the null hypothesis is

accepted.

ALPHA RISK The probability of accepting the alternate hypothesiswhen, in reality, the null hypothesis is true.

ALTERNATE HYPOTHESIS A tentative explanation which indicates that an eventdoes not follow a chance distribution; a contrast to thenull hypothesis.

ANALY

SIS OF VARIANCE A statistical method for evaluating the effect that factors(ANOVA) have on process mean and for evaluating the differencesbetween the means of two or more normal distributions.

ASSIGNABLE CAUSE A process input variable that can be identified and thatcontributes in an observable manner to non-random shiftsin process mean and /or standard deviation.

ASSIGNABLE VARIATIONS Variations in data which can be attributed to specific

causes.

ATTRIBUTE DATA Quality data that typically reflects the number of conforming or non-conforming units or the number of non-conformities per unit on a go/no go or accept/ rejectbasis.

AVERAGE Sum of all measurements divided by the total number of measurements. Statistic which is used to estimate the

population mean. Same as MEAN.


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BACKGROUND VARIABLES Variables which are of no experimental interest and are

not held constant. Their effects are often assumedinsignificant or negligible, or they are randomized to

ensure that contamination of the primary response doesnot occur. Also referred to as environmental variablesand uncontrolled variables.

BENCHMARKING A process for identification of external best-in-classpractices and standards for comparison against internalpractices.

BETA RISK The probability of accepting the null hypothesis when, in

reality, the alternate hypothesis is true.

BINOMIAL DISTRIBUTION A statistical distribution associated with data that is one oftwo possible states such as Go-No Go or Pass-Fail. It isalso the distribution generated by rolling dice.

BLACK BELT A process improvement project team leader who istrained and certified in Six Sigma methodology and toolsand who is responsible for successful project execution.

BLOCKING VARIABLES A relatively homogenous set of conditions within whichdifferent conditions of the primary variables arecompared. Used to ensure that background variables donot contaminate the evaluation of primary variables.

BRAINSTORMING A team-oriented meeting used in problem solving todevelop a list of possible causes that may be linked to anobserved effect.

CAPABILITY INDICES A mathematical calculation used to compare the processvariation to a specification. Examples are Cp, Cpk, Pp,PpK, Zst, and Zlt. GE uses Zst & Zlt as the commoncommunication language on process capability.

CAUSALITY The principle that every change implies the operation of acause.

CAUSATIVE Effective as a cause.

CAUSE That which produces an effect or brings about a change.


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CAUSE AND EFFECT (C&E) One of the seven basic tools for problem solving and is

DIAGRAM sometimes referred to as a fishbonediagram because of its structure. Spine represents the effect and the

major legs of the structure are the cause categories.The substructure represents the list of potential causeswhich can induce the effect. The 6Ms (man, machine,material, method, measurements and mother nature, aresometimes used as cause categories.

C CHARTS Charts which display the number of defects per sample.Used where sample size is constant.

CENTER LINE The line on a statistical process control chart whichrepresents the characteristics central tendency.

CENTRAL TENDENCY Numerical average, e.g., mean, median, and mode;center line on a statistical process control chart.

CHAMPION An executive level business leader who facilitates theleadership, implementation, and deployment of Six Sigmaphilosophies.

CHANGE ACCELERATION A process which helps accelerate stakeholder buy-in andPROGRAM PROGRAM (CAP) implementation of new philosophies and processes within

a business.

CHARACTERISTIC A definable or measurable feature of a process, product,or service.

CHI-SQUARE See x (symbol glossary).

CLASSIFICATION Differentiation of variables.

COMMON CAUSE See RANDOM CAUSE.

CONFIDENCE LEVEL The probability that a randomly distributed variable x lieswithin a defined interval of a normal curve.

CONFIDENCE LIMITS The two values that define the confidence interval.

CONFOUNDING Allowing two or more variables to vary together so that itis impossible to separate their unique effects.


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CONSUMERS RISK Probability of accepting a lot when, in fact, the lot should

have been rejected (see BETA RISK).

CONTINUOUS DATA Data obtained from a measurement system which has aninfinite number of possible outcomes.

CONTINUOUS RANDOM A random variable which can assume any valueVARIABLE continuously within some specified

interval.

CONTROL CHART A graphical rendition of a characteristics performance

across time in relation to its natural limits and centraltendency.

CONTOL LIMITS Apply to both range or standard deviation and subgroupaverage (X) portions of process control charts and areused to determine the state of statistical control. Controllimits are derived statistically and are not related toengineering specification limits in any way.

CONTROL PLAN A formal quality document that describes all of the

elements required to control variations in a particular

process or could apply to a complete product or family ofproducts.

CONTROL SPECIFICATIONS Specification requirements for the product beingmanufactured.

CORRELATION The relationship between two sets of data such that whenone changes, the other is likely to make a correspondingchange. Also, a statistical tool for determining the

relationship between two sets of data.

COST OF POOR QUALITY Cost associated with providing poor quality products or(COPQ) services. Can be divided into four cost

categories: Appraisal, Scrap, Rework, and Field Complaint(warranty costs).

CRITICAL TO QUALITY (CTQ) A drawing characteristic determined to be important forCHARACTERISTIC variability reduction based

on a requirement from production, engineering, customer application, orregulatory agency. Can also apply to transactional orservice delivery processes.


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CUTOFF POINT The point which partitions the acceptance region from the

reject region.

DATA Factual information used as a basis for reasoning,discussion, or calculation; often refers to quantitativeinformation.

DATA TRANSFORMATION A mathematical technique used to create a near normallydistributed data set out of a non-normal (skewed) dataset.

DEFECT Any product characteristic that deviates outside of specification limits.

DEFECT PER MILLION Quality metric used in the Six Sigma process and isOPPORTUNITIES (DPMO) calculated by the number of defects observed divided by

the number of opportunities for defects normalized to 1million units.

DEGREES OF FREEDOM The number of independent measurements available forestimating a population parameter.

DENSITY FUNCTION The function which yields the probability that a particularrandom variable takes on any one of its possible values.

DEPENDENT VARIABLE A Response Variable; e.g., y is the dependent orResponse variable where Y= f(X1. . .XN) process inputvariables.

DESIGN OF EXPERIMENT A formal, proactive method for documenting the selected(DOE) controllable factors and their levels, as

well as establishing blocks, replications and responsevariables associated with a planned experiment. It is the planfor conducting the experiment and evaluating the results.

DISCRETE DATA Data obtained from a measurement system which has afinite number of possible outcomes.

DISCRETE RANDOM VARIABLE A random variable which can assume values only from adefinite number of discrete values.


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DISTRIBUTIONS Tendency of large numbers of observations to group

themselves around some central value with a certainamount of variation or scatter on either side.

EFFECT That which was produced by a cause.

EVOLUTIONARYOPERATIONS A DOE process used to optimize the key process input(EVOPS) variables in a production environment, is usuallylimited to 2-3 variables, is performed over a long period oftime, and is non-disruptive to the process.

EXCEL Spreadsheet package within Microsoft Office used for data manipulation & analysis.

EXPERIMENT A test under defined conditions to determine an unknowneffect, to illustrate or verify a known law, or to establish

a hypothesis. See DESIGN OF EXPERIMENT (DOE).

EXPERIMENTAL ERROR Variation in observations made under identical testconditions. Also called residual error. The amount ofvariation which cannot be attributed to the variables

included in the experiment.

EXPONENTIALLYWEIGHTED A control charting method where the most current dataMOVING AVERAGE (EWMA) point is

weighted on an exponential basis such that older datapoints carry less value in calculating average. Thischarting technique is used to detect small shifts in processaverage.

FACTORS Independent variables.

FAILURE MODE & EFFECTS Analytical technique focused at problem prevention thruANALYSIS (FMEA) identification of potential problems.The FMEA is a proactive tool that is used pragmatically toidentify potential failure modes and their effects, tonumerically rate the combined risk associated withseverity, probability of occurrence and delectability and to

document appropriate plans for prevention. FMEAscan be applied to system, (application) and

product design and to manufacturing and non-manufacturing processes (i.e., services &transactional processes).


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FIRST TIME YIELD Yield that occurs in any process step prior to any rework

that may be required (see Yft Symbology) to overcomeprocess shortcomings.

FIXED EFFECTS MODEL An experimental model where treatments are specificallyselected by the researcher. Conclusions only apply to thefactor levels considered in the analysis. Inferences arerestricted to the experimental levels.

FLUCTUATIONS Variances in data which are caused by a large number of minute variations or differences.

FREQUENCY DISTRIBUTION The pattern or shape formed by the group ofmeasurements in a distribution based on frequency ofoccurrence.

GAGE ACCURACY The average difference observed between a gage underevaluation and a master gage when measuring the sameparts over multiple readings.

GAGE LINEARITY A measure of gage accuracy variation when evaluated

over the expected operating range.

GAGE REPEATABILITY A measure of the variation observed when a singleoperator uses a gage to measure a group of randomlyordered (but identifiable) parts on a repetitive basis.

GAGE REPRODUCIBILITY A measure of average variation observed betweenoperations when multiple operators use the same gage tomeasure a group of randomly ordered (but identifiable)parts on a repetitive basis.

GAGE STABILITY A measure of variation observed when a gage is used tomeasure the same master over an extended period oftime.

GREEN BELT Six Sigma role similar in function to Black Belt but lengthof training and project scope are reduced.

HISTOGRAM Vertical display of a population distribution in terms of

frequencies; a formal method of plotting a frequencydistribution.

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HOMOGENEITYOF VARIANCE The variances of the data groups being contrasted are

equal (as defined by a statistical test of significantdifference).

HYPOTHESIS When used as a statistical term, it is a theory proposedor postulated for comparing means and standarddeviations of two or more data sets. A null hypothesisstates that the data sets are from the same statisticalpopulation, while the alternate hypothesis states that thedata sets are not from the same statistical population.

INDEPENDENT VARIABLE A controlled variable; a variable whose value isindependent of the value of another variable.

INSTABILITY Unnaturally large fluctuations in a process input or outputcharacteristic.

INTERACTION The tendency of two or more variables to produce aneffect in combination which neither variable wouldproduce if acting alone.

INTERVAL Numeric categories with equal units of measure but no

absolute zero point, i.e., quality scale or index.

KEY NOISE PARAMETERS Variables which are Hard or Expensive to control.

KEY PROCESS INPUT The vital few input variables, called xs, (normally 2-6)VARIABLES (KPIVS) that drive 80% of the observed variations in the process

output characteristic (y). a.k.a Key Control Parameters

LINE CHARTS Charts used to track the performance without relationship

to process capability or control limits.

LOWER CONTROL LIMIT A horizontal dotted line plotted on a control chart whichrepresents the lowest process deviation that should occurif the process is in control (free from assignable causevariation).

MASTER BLACK BELT A person who is expert on Six Sigma techniques and onproject implementation. Master Black Belts play a major

role in training, coaching and in removing barriers tosuccessful project execution in addition to overallpromotion of the Six Sigma philosophy.

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MEAN See AVERAGE.

MEAN TIME BETWEEN Average time to failure for a statistically significant

FAILURES (MTBF) population of product operating in its normal environment.

MEASUREMENT SYSTEMS Means of evaluating a continuous or discreteANALYSIS (MSA) measurement system to quantify the amount of variation

contributed by the measurement system. Refer toAutomotive Std. (AIAG STD) for details.

MEDIAN The mid value in a group of measurements when ordered

from low to high.

MINITAB Statistical software package that operates on MicrosoftWindows with a spreadsheet format and has powerfulstatistical analysis ability.

MISTAKE PROOFING Mistake proofing is a proactive technique used topositively prevent errors from occurring.

MIXED EFFECTS MODEL Contains elements of both the fixed and random effects

models.

MULTI-VARI Method used in the measure/analyze phase of Six Sigmato display in graphical terms the variation within parts,machines, or processes between machines or processparts, and over time.

NONCONFORMING UNIT A unit which does not conform to one or morespecifications, standards, and/or requirements.

NONCONFORMITY A condition within a unit which does not conform to somespecific specification, standard, and/or requirement; oftenreferred to as a defect; any given nonconforming unit canhave the potential for more than one nonconformity.

NORMAL DISTRIBUTION A continuous, symmetrical density function characterizedby a bell-shaped curve, e.g., distribution of samplingaverages.

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NORMALIZED ROLLED The estimate of the average process yield used to

THROUGHPUTYIELD (RYTN) determine RTY. It is determined by taking the nth root ofthe RTY (where n is the # process step) included in the

RTY

calculation.

NULL HYPOTHESIS An assertion to be proven by statistical analysis wheretwo or more data sets are stated to be from the samepopulation.

ONE-SIDED ALTERNATIVE The value of a parameter which has an upper bound or alower bound, but not both.

ORDINAL Ordered categories (ranking) with no information aboutdistance between each category, i.e., rank ordering ofseveral measurements of an output parameter.

ORDINATE The vertical axis of a graph.

OUT OF CONTROL Condition which applies to statistical process control chartwhere plot points fall outside of the control limits or fail anestablished run or trend criteria, all of which indicate that

an assignable cause is present in the process.

PARAMETER A constant defining a particular property of the densityfunction of a variable.

PARETO DIAGRAM A chart which places common occurrences in rank order.

P CHARTS Charts used to plot percent defectives in a sample wheresample size is variable.

PERTURBATION A nonrandom disturbance.

POISSON DISTRIBUTION A statistical distribution associated with attribute data (thenumber of non-continuities found in a unit) and can beused to predict first pass yield.

POPULATION A group of similar items from which a sample is drawn.Often referred to as the universe.

POPULATION The entire set of items from which a sample is drawn.

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POWER OF AN EXPERIMENT The probability of rejecting the null hypothesis when it is

false and accepting the alternate hypothesis when it istrue.

PRECISION TO TOLERANCE A ratio used to express the portion of engineeringRATIO (P/T) specification consumed by the 99% confidence interval of

measurement system repeatability and reproducibilityerror. (5.15 standard deviations of R&R error)

PREVENTION The practice of eliminating unwanted variation before thefact, e.g., predicting a future condition from a control

chart and then applying corrective action before thepredicted event transpires.

PRIMARY CONTROL The major independent variables used in the experiment.VARIABLES

PROBABILITY The chance of an event happening or condition occurringby pure chance and is stated in numerical form.

PROBABILITYOF AN EVENT The number of successful events divided by the total

number of trials.

PROBLEM A deviation from a specified standard.

PROBLEM SOLVING The process of solving problems; the isolation and controlof those conditions which generate or facilitate thecreation of undesirable symptoms.

PROCESS A particular method of doing something, generallyinvolving a number of steps or operations.

PROCESS AVERAGE The central tendency of a given process characteristicacross a given amount of time or at a specific point intime.

PROCESS CONTROL See STATISTICAL PROCESS CONTROL.

PROCESS CONTROL CHART Any of a number of various types of graphs upon whichdata are plotted against specific control limits.

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PROCESS MAP A detailed step-by-step pictorial sequence of a process

showing process inputs, potential or actual controllableand uncontrollable sources of variation, process outputs,cycle time, rework operations, and inspection points.

PROCESS SPREAD The range of values which a given process characteristicdisplays; this particular term most often applies to therange but may also encompass the variance. The spreadmay be based on a set of data collected at a specificpoint in time or may reflect the variability across a givenperiod of time.

PRODUCERS RISK Probability of rejecting a lot when, in fact, the lot shouldhave been accepted (see ALPHA RISK).

PROJECT A problem, usually calling for planned action.

QUALITY FUNCTION QFD is a disciplined matrix methodology used for DEPLOYMENT (QFD) documenting customer wants and needs the voice of

the customer into operational requirement terms. It isan effective tool for determining critical-to-quality

characteristics for transactional processes, services and

products.

R CHART Plot of the difference between the highest and lowest in asample. Normally associated with the range controlportion of an X, R chart.

RANDOM CAUSE A source of variation which is random, usually associatedwith the trivial many process input variables, and whichwill not produce a highly predictable change in the

process output response (dependent variable), e.g., acorrelation does not exist; any individual source ofvariation results in a small amount of variation in theresponse; cannot be economically eliminated from aprocess; an inherent natural source of variation.

RANDOMNESS A condition in which any individual event in a set of events has the same mathematical probability ofoccurrence as all other events within the specified set,

i.e., individual events are not predictable even thoughthey may collectively belong to a definable distribution.

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RANDOM SAMPLE One or more samples randomly selected from theuniverse (population).

RANDOM SAMPLE Selecting a sample such that each item in the populationhas an equal chance of being selected; lack ofpredictability; without pattern.

RANDOM VARIABLE A variable which can assume any value from adistribution which represents a set of possible values.

RANDOM VARIATIONS Variations in data which result from causes which cannotbe pinpointed or controlled.

RANGE The difference between the highest and lowest values ina subgroup sample.

RANK Values assigned to items in a sample to determine their relative occurrence in a population.

RATIONAL SUBGROUP A subgroup is usually made up of consecutive pieces

chosen from the process stream so that the variation

represented within each subgroup is as small as feasible.Any changes, shifts and drifts in the process will appearas differences between the subgroups, selected overtime.

REGRESSION A statistical technique for determining the bestmathematical expression that describes the functionalrelationship between one response and one or moreindependent variables.

REJECT REGION The region of values for which the alternate hypothesis isaccepted.

REPLICATION Repeat observations made under identical testconditions.

REPRESENTATIVE SAMPLE A sample which accurately reflects a specific condition orset of conditions within the universe.

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RESEARCH Critical and exhaustive investigation or experimentation

having for its aim the revision of accepted conclusions inthe light of newly discovered facts.

RESIDUAL ERROR See EXPERIMENTAL ERROR.

RESPONSE SURFACE A graphical (pictorial) analysis technique used inMETHODOLOGY (RSM) conjunction with DOE fordetermining optimum process parameter settings.

ROBUST The condition or state in which a response parameter

exhibits a high degree of resistance to external causes ofa nonrandom nature; i.e., impervious to perturbing

influence.

ROLLED THROUGHPUTYIELD The product (series multiplication) of all of the individual(RTY) first pass yields of each step of the total process.

ROOT SUM SQUARED (RSS) Square root of the sum of the squares. Means ofcombining standard deviations from independent causes.

SAMPLE A portion of a population of data chosen to estimate some

characteristic about the whole population. One or moreobservations drawn from a larger collection ofobservations or universe (population).

SCATTER DIAGRAMS Charts which allow the study of correlation, e.g., therelationship between two variables or data sets.

SHORT RUN STATISTICAL A statistical control charting technique which applies toPROCESS CONTROL any process situation where there

is insufficient frequency of subgroup data to use traditionalcontrol charts (typically associated with low-volumemanufacturing or where setups occur frequently). Multiplepart numbers and multiple process streams can be plottedon a single chart.

SIX MS The major categories that contribute to effects on thefishbone diagram (man, machine, material, method,measurement, and mother nature.

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SIX SIGMA A term coined by Motorola to express process capability

in parts per million. A Six Sigma process generates amaximum defect probability of 3.4 parts per million

(PPM) when the amount of process shifts and drifts arecontrolled over the long term to less than +1.5

standard deviations.

SKEWED DISTRIBUTION A non-symmetrical distribution having a tail in either apositive or negative direction.

SPECIAL CAUSE See ASSIGNABLE CAUSE.

STABLE PROCESS A process which is free of assignable causes, e.g., in

statistical control.

STANDARD DEVIATION A statistical index of variability which describes theprocess spread or width of distribution.

STATISTICAL CONTROL A quantitative condition which describes a process that isfree of assignable/special causes of variation (both

mean and standard deviation). Such a condition is most

often evidenced on a control chart, i.e., a control chart

which displays an absence of nonrandom variation.

STATISTICAL PROCESS The application of standardized statistical methods andCONTROL (SPC) procedures to a process for controlpurposes.

SUBGROUP A logical grouping of objects or events which displaysonly random event-to-event variations, e.g., the

objects or events are grouped to create homogenous

groups free of assignable or special causes. By virtue ofminimizing within subgroup variability, any change in thecentral tendency or variance of the universe will bereflected in the subgroup-to-subgroup variability.

A predetermined sample of consecutive parts or otherdata bearing objects removed from the processfor the purpose of data collection.

SY

MPTOM That which serves as evidence of something not fullyunderstood in factual terms.

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SYSTEM That which is connected according to a scheme.

SYSTEMATIC VARIABLES A pattern which displays predictable tendencies.

TEST OF SIGNIFICANCE A statistical procedure used to determine whether or not aprocess observation (data set) differs from a postulated

value by an amount greater than that due to random

variation alone.

THEORY A plausible or scientifically acceptable general principle

offered to explain phenomena.

TWO-SIDED ALTERNATIVE The values of a parameter which designate both an upper

and lower bound.

TYPE I ERROR See ALPHA RISK.

TYPE II ERROR See BETA RISK.

UNNATURAL PATTERN Any pattern in which a significant number of the

measurements do not group themselves around a central

tendency. When the pattern is unnatural, it means that

non-random disturbances are present and are affecting

the process.

UPPER CONTROL LIMIT A horizontal line on a control chart (usually dotted) which

represents the upper limits of capability for a process

operating with only random variation.

VARIABLE A characteristic that may take on different values.

VARIABLES DATA Data collected from a process input or output where the

measurement scale has a significant level of subdivisions

or resolution, e.g., ohms, voltage, diameter, etc.

VARIATION Any quantifiable difference between individual

measurements; such differences can be classified as

being due to common causes (random) or special causes(assignable).

VARIATION RESEARCH Procedures, techniques, and methods used to isolate one

type of variation from another (for example, separating

product variation from test variation).

X & R CHARTS A control chart which is a representation of processcapability over time; displays the variability in the process

average and range across time.

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